Book Image

Keras 2.x Projects

By : Giuseppe Ciaburro
Book Image

Keras 2.x Projects

By: Giuseppe Ciaburro

Overview of this book

Keras 2.x Projects explains how to leverage the power of Keras to build and train state-of-the-art deep learning models through a series of practical projects that look at a range of real-world application areas. To begin with, you will quickly set up a deep learning environment by installing the Keras library. Through each of the projects, you will explore and learn the advanced concepts of deep learning and will learn how to compute and run your deep learning models using the advanced offerings of Keras. You will train fully-connected multilayer networks, convolutional neural networks, recurrent neural networks, autoencoders and generative adversarial networks using real-world training datasets. The projects you will undertake are all based on real-world scenarios of all complexity levels, covering topics such as language recognition, stock volatility, energy consumption prediction, faster object classification for self-driving vehicles, and more. By the end of this book, you will be well versed with deep learning and its implementation with Keras. You will have all the knowledge you need to train your own deep learning models to solve different kinds of problems.
Table of Contents (13 chapters)

Multiple linear regression concepts

So far, we have resolved simple linear regression problems that study the relation between a dependent variable, y, and an independent variable, x, based on the following regression equation:

In this equation, the explanatory variable is represented by x and the response variable is represented by y. To solve this problem, the least squares method was used. In this method, we can find the best fit by minimizing the sum of squares of the vertical distances from each data point on the line. As mentioned previously, we don't find that a variable depends solely on another very often. Usually, we find that the response variable depends on at least two predictors. In practice, we will have to create models with a response variable that depend on more than one predictor. These models are...